diff --git a/test.py b/test.py
index 3c62302..5e975aa 100755
--- a/test.py
+++ b/test.py
@@ -17,75 +17,39 @@ import columbus
 root_path = '.'
 
 
-def main(env_name='ColumbusCandyland_Aux10-v0', timesteps=500000, showRes=True, saveModel=True, n_eval_episodes=0):
+def main(env_name='ColumbusCandyland_Aux10-v0', timesteps=200_000, showRes=True, saveModel=True, n_eval_episodes=0):
     env = gym.make(env_name)
-    test_sde = False
+    use_sde = False
     ppo = PPO(
         "MlpPolicy",
         env,
         verbose=0,
-        tensorboard_log=root_path+"/logs_tb/"+env_name+"/ppo/",
+        tensorboard_log=root_path+"/logs_tb/" +
+        env_name+"/ppo"+(['', '_sde'][use_sde])+"/",
         learning_rate=3e-4,
         gamma=0.99,
         gae_lambda=0.95,
         normalize_advantage=True,
-        ent_coef=0.1,  # 0.1
+        ent_coef=0.02,  # 0.1
         vf_coef=0.5,
-        use_sde=False,  # False
+        use_sde=use_sde,  # False
+        clip_range=0.2,
     )
     trl_pg = TRL_PG(
         "MlpPolicy",
         env,
         verbose=0,
-        tensorboard_log=root_path+"/logs_tb/"+env_name+"/trl_pg/",
+        tensorboard_log=root_path+"/logs_tb/"+env_name +
+        "/trl_pg"+(['', '_sde'][use_sde])+"/",
         learning_rate=3e-4,
         gamma=0.99,
         gae_lambda=0.95,
         normalize_advantage=True,
-        ent_coef=0.1,  # 0.1
+        ent_coef=0.03,  # 0.1
         vf_coef=0.5,
-        use_sde=False,  # False
+        use_sde=use_sde,
+        clip_range=2,  # 0.2
     )
-    if test_sde:
-        ppo_latent_sde = PPO(
-            "MlpPolicy",
-            env,
-            verbose=0,
-            tensorboard_log=root_path+"/logs_tb/"+env_name+"/ppo_latent_sde/",
-            learning_rate=3e-4,
-            gamma=0.99,
-            gae_lambda=0.95,
-            normalize_advantage=True,
-            ent_coef=0.15,  # 0.1
-            vf_coef=0.5,
-            use_sde=True,  # False
-            sde_sample_freq=30*15,  # -1
-        )
-        trl_pg_latent_sde = TRL_PG(
-            "MlpPolicy",
-            env,
-            verbose=0,
-            tensorboard_log=root_path+"/logs_tb/"+env_name+"/trl_pg_latent_sde/",
-            learning_rate=3e-4,
-            gamma=0.99,
-            gae_lambda=0.95,
-            normalize_advantage=True,
-            ent_coef=0.15,  # 0.1
-            vf_coef=0.5,
-            use_sde=True,  # False
-            sde_sample_freq=30*15,  # -1
-        )
-    # sac_latent_sde = SAC(
-    #    "MlpPolicy",
-    #    env,
-    #    verbose=0,
-    #    tensorboard_log=root_path+"/logs_tb/"+env_name+"/sac_latent_sde/",
-    #    use_sde=True,
-    #    sde_sample_freq=30*15,
-    #    ent_coef=0.0016, #0.0032
-    #    gamma=0.99, # 0.95
-    #    learning_rate=0.001 # 0.015
-    # )
 
     print('TRL_PG:')
     testModel(trl_pg, timesteps, showRes,
@@ -97,13 +61,18 @@ def main(env_name='ColumbusCandyland_Aux10-v0', timesteps=500000, showRes=True,
 
 def testModel(model, timesteps, showRes=False, saveModel=False, n_eval_episodes=16):
     env = model.get_env()
-    model.learn(timesteps)
+    try:
+        model.learn(timesteps)
+    except KeyboardInterrupt:
+        print('[!] Training Terminated')
+        pass
 
     if saveModel:
         now = datetime.datetime.now().strftime('%d.%m.%Y-%H:%M')
         loc = root_path+'/models/' + \
             model.tensorboard_log.replace(
                 root_path+'/logs_tb/', '').replace('/', '_')+now+'.zip'
+        print(model.get_parameters())
         model.save(loc)
 
     if n_eval_episodes:
@@ -132,3 +101,6 @@ def testModel(model, timesteps, showRes=False, saveModel=False, n_eval_episodes=
 
 if __name__ == '__main__':
     main('LunarLanderContinuous-v2')
+    #main('ColumbusJustState-v0')
+    #main('ColumbusStateWithBarriers-v0')
+    #main('ColumbusEasierObstacles-v0')